Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Björn Lütjens"'
Autor:
Björn Lütjens, Patrick Alexander, Raf Antwerpen, Guido Cervone, Matthew Kearney, Bingkun Luo, Dava Newman, Marco Tedesco
Motivation. Ice melting in Greenland and Antarctica has increasingly contributed to rising sea levels. Yet, the exact speed of melting, existence of abrupt tipping points, and in-detail links to climate change remain uncertain. Ice shelves essentiall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82ed7eda1f93ce57c787148dbf7e6de3
https://doi.org/10.5194/egusphere-egu23-4044
https://doi.org/10.5194/egusphere-egu23-4044
Adjoints have become a staple of the oceanic and atmospheric numerical modeling community over the past couple of decades as they are useful for tuning of dynamical models, sensitivity analyses, and data assimilation. One such application is generati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5772d2ebe544f21acc9844fdad6dc988
https://doi.org/10.5194/egusphere-egu23-10810
https://doi.org/10.5194/egusphere-egu23-10810
Running a high-resolution global climate model can take multiple days on the world's largest supercomputers. Due to the long runtimes that are caused by solving the underlying partial differential equations (PDEs), climate researchers struggle to gen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ab27c64a5abf9c80743680328ebf575
https://doi.org/10.5194/egusphere-egu22-8499
https://doi.org/10.5194/egusphere-egu22-8499
Autor:
Emma Erickson, Willa Potosnak, Arthur Fender C. Bucker, Björn Lütjens, Salva Rühling Cachay, Ernest Pokropek, Salomey Osei
Deep learning-based models have been recently shown to be competitive with, or even outperform, state-of-the-art long range forecasting models, such as for projecting the El Niño-Southern Oscillation (ENSO). However, current deep learning models are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02f8d6173d9021e928a2a7597c3c020a
https://doi.org/10.5194/egusphere-egu21-9141
https://doi.org/10.5194/egusphere-egu21-9141
Publikováno v:
arXiv
Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from nois